Overview

Dataset statistics

Number of variables43
Number of observations1339
Missing cells3990
Missing cells (%)6.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory1.8 KiB

Variable types

Categorical24
Numeric19

Alerts

Owner has a high cardinality: 315 distinct values High cardinality
Farm.Name has a high cardinality: 571 distinct values High cardinality
Lot.Number has a high cardinality: 227 distinct values High cardinality
Mill has a high cardinality: 459 distinct values High cardinality
ICO.Number has a high cardinality: 847 distinct values High cardinality
Company has a high cardinality: 281 distinct values High cardinality
Altitude has a high cardinality: 396 distinct values High cardinality
Region has a high cardinality: 356 distinct values High cardinality
Producer has a high cardinality: 692 distinct values High cardinality
Bag.Weight has a high cardinality: 56 distinct values High cardinality
Grading.Date has a high cardinality: 567 distinct values High cardinality
Owner.1 has a high cardinality: 319 distinct values High cardinality
Expiration has a high cardinality: 566 distinct values High cardinality
Aroma is highly correlated with Flavor and 6 other fieldsHigh correlation
Flavor is highly correlated with Aroma and 6 other fieldsHigh correlation
Aftertaste is highly correlated with Aroma and 6 other fieldsHigh correlation
Acidity is highly correlated with Aroma and 6 other fieldsHigh correlation
Body is highly correlated with Aroma and 6 other fieldsHigh correlation
Balance is highly correlated with Aroma and 6 other fieldsHigh correlation
Uniformity is highly correlated with Clean.CupHigh correlation
Clean.Cup is highly correlated with UniformityHigh correlation
Cupper.Points is highly correlated with Aroma and 6 other fieldsHigh correlation
Total.Cup.Points is highly correlated with Aroma and 6 other fieldsHigh correlation
altitude_low_meters is highly correlated with altitude_high_meters and 1 other fieldsHigh correlation
altitude_high_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
altitude_mean_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
Aroma is highly correlated with Flavor and 6 other fieldsHigh correlation
Flavor is highly correlated with Aroma and 6 other fieldsHigh correlation
Aftertaste is highly correlated with Aroma and 6 other fieldsHigh correlation
Acidity is highly correlated with Aroma and 6 other fieldsHigh correlation
Body is highly correlated with Aroma and 6 other fieldsHigh correlation
Balance is highly correlated with Aroma and 6 other fieldsHigh correlation
Uniformity is highly correlated with Clean.Cup and 1 other fieldsHigh correlation
Clean.Cup is highly correlated with Uniformity and 1 other fieldsHigh correlation
Sweetness is highly correlated with Total.Cup.PointsHigh correlation
Cupper.Points is highly correlated with Aroma and 6 other fieldsHigh correlation
Total.Cup.Points is highly correlated with Aroma and 9 other fieldsHigh correlation
altitude_low_meters is highly correlated with altitude_high_meters and 1 other fieldsHigh correlation
altitude_high_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
altitude_mean_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
Aroma is highly correlated with Flavor and 3 other fieldsHigh correlation
Flavor is highly correlated with Aroma and 6 other fieldsHigh correlation
Aftertaste is highly correlated with Aroma and 6 other fieldsHigh correlation
Acidity is highly correlated with Flavor and 4 other fieldsHigh correlation
Body is highly correlated with Flavor and 4 other fieldsHigh correlation
Balance is highly correlated with Flavor and 5 other fieldsHigh correlation
Uniformity is highly correlated with Clean.CupHigh correlation
Clean.Cup is highly correlated with UniformityHigh correlation
Cupper.Points is highly correlated with Aroma and 6 other fieldsHigh correlation
Total.Cup.Points is highly correlated with Aroma and 6 other fieldsHigh correlation
altitude_low_meters is highly correlated with altitude_high_meters and 1 other fieldsHigh correlation
altitude_high_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
altitude_mean_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
Bag.Weight is highly correlated with unit_of_measurementHigh correlation
Variety is highly correlated with unit_of_measurementHigh correlation
Country.of.Origin is highly correlated with Certification.Address and 5 other fieldsHigh correlation
Certification.Address is highly correlated with Country.of.Origin and 5 other fieldsHigh correlation
Species is highly correlated with Country.of.Origin and 2 other fieldsHigh correlation
Certification.Body is highly correlated with Country.of.Origin and 4 other fieldsHigh correlation
Certification.Contact is highly correlated with Country.of.Origin and 5 other fieldsHigh correlation
In.Country.Partner is highly correlated with Country.of.Origin and 4 other fieldsHigh correlation
unit_of_measurement is highly correlated with Bag.Weight and 6 other fieldsHigh correlation
Species is highly correlated with Country.of.Origin and 3 other fieldsHigh correlation
Country.of.Origin is highly correlated with Species and 16 other fieldsHigh correlation
Number.of.Bags is highly correlated with Country.of.Origin and 6 other fieldsHigh correlation
Bag.Weight is highly correlated with Country.of.Origin and 11 other fieldsHigh correlation
In.Country.Partner is highly correlated with Country.of.Origin and 11 other fieldsHigh correlation
Harvest.Year is highly correlated with Country.of.Origin and 8 other fieldsHigh correlation
Variety is highly correlated with Country.of.Origin and 9 other fieldsHigh correlation
Processing.Method is highly correlated with Country.of.Origin and 6 other fieldsHigh correlation
Aroma is highly correlated with Flavor and 9 other fieldsHigh correlation
Flavor is highly correlated with Aroma and 9 other fieldsHigh correlation
Aftertaste is highly correlated with Country.of.Origin and 10 other fieldsHigh correlation
Acidity is highly correlated with Aroma and 9 other fieldsHigh correlation
Body is highly correlated with Country.of.Origin and 10 other fieldsHigh correlation
Balance is highly correlated with Country.of.Origin and 10 other fieldsHigh correlation
Uniformity is highly correlated with Aroma and 9 other fieldsHigh correlation
Clean.Cup is highly correlated with Aroma and 9 other fieldsHigh correlation
Sweetness is highly correlated with Species and 13 other fieldsHigh correlation
Cupper.Points is highly correlated with Bag.Weight and 11 other fieldsHigh correlation
Total.Cup.Points is highly correlated with Aroma and 9 other fieldsHigh correlation
Moisture is highly correlated with Country.of.Origin and 6 other fieldsHigh correlation
Category.One.Defects is highly correlated with Certification.Address and 1 other fieldsHigh correlation
Quakers is highly correlated with altitude_low_meters and 2 other fieldsHigh correlation
Color is highly correlated with Country.of.Origin and 4 other fieldsHigh correlation
Category.Two.Defects is highly correlated with Certification.Address and 1 other fieldsHigh correlation
Certification.Body is highly correlated with Country.of.Origin and 11 other fieldsHigh correlation
Certification.Address is highly correlated with Species and 15 other fieldsHigh correlation
Certification.Contact is highly correlated with Species and 15 other fieldsHigh correlation
unit_of_measurement is highly correlated with Country.of.Origin and 6 other fieldsHigh correlation
altitude_low_meters is highly correlated with Quakers and 2 other fieldsHigh correlation
altitude_high_meters is highly correlated with Quakers and 2 other fieldsHigh correlation
altitude_mean_meters is highly correlated with Quakers and 2 other fieldsHigh correlation
Farm.Name has 359 (26.8%) missing values Missing
Lot.Number has 1063 (79.4%) missing values Missing
Mill has 318 (23.7%) missing values Missing
ICO.Number has 157 (11.7%) missing values Missing
Company has 209 (15.6%) missing values Missing
Altitude has 226 (16.9%) missing values Missing
Region has 59 (4.4%) missing values Missing
Producer has 232 (17.3%) missing values Missing
Harvest.Year has 47 (3.5%) missing values Missing
Variety has 226 (16.9%) missing values Missing
Processing.Method has 170 (12.7%) missing values Missing
Color has 218 (16.3%) missing values Missing
altitude_low_meters has 230 (17.2%) missing values Missing
altitude_high_meters has 230 (17.2%) missing values Missing
altitude_mean_meters has 230 (17.2%) missing values Missing
altitude_low_meters is highly skewed (γ1 = 20.3231837) Skewed
altitude_high_meters is highly skewed (γ1 = 20.31093398) Skewed
altitude_mean_meters is highly skewed (γ1 = 20.32052739) Skewed
Moisture has 264 (19.7%) zeros Zeros
Category.One.Defects has 1137 (84.9%) zeros Zeros
Quakers has 1244 (92.9%) zeros Zeros
Category.Two.Defects has 373 (27.9%) zeros Zeros

Reproduction

Analysis started2022-03-15 14:45:51.722969
Analysis finished2022-03-15 14:47:10.562455
Duration1 minute and 18.84 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Species
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
Arabica
1311 
Robusta
 
28

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArabica
2nd rowArabica
3rd rowArabica
4th rowArabica
5th rowArabica

Common Values

ValueCountFrequency (%)
Arabica1311
97.9%
Robusta28
 
2.1%

Length

2022-03-15T09:47:10.817686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T09:47:10.913083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
arabica1311
97.9%
robusta28
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Owner
Categorical

HIGH CARDINALITY

Distinct315
Distinct (%)23.6%
Missing7
Missing (%)0.5%
Memory size117.1 KiB
juan luis alvarado romero
155 
racafe & cia s.c.a
 
60
exportadora de cafe condor s.a
 
54
kona pacific farmers cooperative
 
52
ipanema coffees
 
50
Other values (310)
961 

Length

Max length50
Median length22
Mean length21.21321321
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)12.9%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowgrounds for health admin
4th rowyidnekachew dabessa
5th rowmetad plc

Common Values

ValueCountFrequency (%)
juan luis alvarado romero155
 
11.6%
racafe & cia s.c.a60
 
4.5%
exportadora de cafe condor s.a54
 
4.0%
kona pacific farmers cooperative52
 
3.9%
ipanema coffees50
 
3.7%
cqi taiwan icp cqi台灣合作夥伴47
 
3.5%
lin, che-hao krude 林哲豪30
 
2.2%
nucoffee29
 
2.2%
carcafe ltda ci27
 
2.0%
the coffee source inc.23
 
1.7%
Other values (305)805
60.1%

Length

2022-03-15T09:47:11.067664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
luis169
 
3.9%
juan160
 
3.7%
alvarado155
 
3.6%
romero155
 
3.6%
de114
 
2.6%
s.a101
 
2.3%
coffee87
 
2.0%
cafe74
 
1.7%
exportadora70
 
1.6%
coffees67
 
1.6%
Other values (655)3170
73.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country.of.Origin
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)2.7%
Missing1
Missing (%)0.1%
Memory size96.5 KiB
Mexico
236 
Colombia
183 
Guatemala
181 
Brazil
132 
Taiwan
75 
Other values (31)
531 

Length

Max length28
Median length8
Mean length8.835575486
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.4%

Sample

1st rowEthiopia
2nd rowEthiopia
3rd rowGuatemala
4th rowEthiopia
5th rowEthiopia

Common Values

ValueCountFrequency (%)
Mexico236
17.6%
Colombia183
13.7%
Guatemala181
13.5%
Brazil132
9.9%
Taiwan75
 
5.6%
United States (Hawaii)73
 
5.5%
Honduras53
 
4.0%
Costa Rica51
 
3.8%
Ethiopia44
 
3.3%
Tanzania, United Republic Of40
 
3.0%
Other values (26)270
20.2%

Length

2022-03-15T09:47:11.311391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mexico236
13.9%
colombia183
 
10.8%
guatemala181
 
10.6%
brazil132
 
7.8%
united127
 
7.5%
states87
 
5.1%
taiwan75
 
4.4%
hawaii73
 
4.3%
honduras53
 
3.1%
costa51
 
3.0%
Other values (35)503
29.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Farm.Name
Categorical

HIGH CARDINALITY
MISSING

Distinct571
Distinct (%)58.3%
Missing359
Missing (%)26.8%
Memory size95.5 KiB
various
 
47
rio verde
 
23
several
 
20
finca medina
 
15
doi tung development project
 
13
Other values (566)
862 

Length

Max length73
Median length13
Mean length15.3622449
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique438 ?
Unique (%)44.7%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowsan marcos barrancas "san cristobal cuch
4th rowyidnekachew dabessa coffee plantation
5th rowmetad plc

Common Values

ValueCountFrequency (%)
various47
 
3.5%
rio verde23
 
1.7%
several20
 
1.5%
finca medina15
 
1.1%
doi tung development project13
 
1.0%
fazenda capoeirnha13
 
1.0%
conquista / morito11
 
0.8%
los hicaques11
 
0.8%
capoeirinha10
 
0.7%
el papaturro9
 
0.7%
Other values (561)808
60.3%
(Missing)359
26.8%

Length

2022-03-15T09:47:11.548561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
el96
 
4.0%
la87
 
3.6%
finca79
 
3.3%
coffee74
 
3.1%
various47
 
2.0%
estate44
 
1.8%
santa40
 
1.7%
40
 
1.7%
fazenda39
 
1.6%
los36
 
1.5%
Other values (856)1814
75.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Lot.Number
Categorical

HIGH CARDINALITY
MISSING

Distinct227
Distinct (%)82.2%
Missing1063
Missing (%)79.4%
Memory size62.4 KiB
1
 
18
020/17
 
6
019/17
 
5
103
 
3
2016 Tainan Coffee Cupping Event Micro Lot 臺南市咖啡評鑑批次
 
3
Other values (222)
241 

Length

Max length71
Median length9
Mean length9.891304348
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique205 ?
Unique (%)74.3%

Sample

1st rowYNC-06114
2nd row102
3rd rowTsoustructive 2015 Sumatra Typica
4th row11/23/0252
5th rowBaby Geisha Washed

Common Values

ValueCountFrequency (%)
118
 
1.3%
020/176
 
0.4%
019/175
 
0.4%
1033
 
0.2%
2016 Tainan Coffee Cupping Event Micro Lot 臺南市咖啡評鑑批次3
 
0.2%
23
 
0.2%
1023
 
0.2%
43102245 - P46152
 
0.1%
632
 
0.1%
472
 
0.1%
Other values (217)229
 
17.1%
(Missing)1063
79.4%

Length

2022-03-15T09:47:11.754013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
119
 
4.9%
11
 
2.8%
coffee10
 
2.6%
020/176
 
1.5%
lot6
 
1.5%
tainan6
 
1.5%
20175
 
1.3%
event5
 
1.3%
019/175
 
1.3%
evaluation5
 
1.3%
Other values (257)313
80.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Mill
Categorical

HIGH CARDINALITY
MISSING

Distinct459
Distinct (%)45.0%
Missing318
Missing (%)23.7%
Memory size100.4 KiB
beneficio ixchel
90 
dry mill
 
39
trilladora boananza
 
38
ipanema coffees
 
16
neiva
 
15
Other values (454)
823 

Length

Max length77
Median length17
Mean length19.06170421
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique309 ?
Unique (%)30.3%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowwolensu
4th rowmetad plc
5th rowhvc

Common Values

ValueCountFrequency (%)
beneficio ixchel90
 
6.7%
dry mill39
 
2.9%
trilladora boananza38
 
2.8%
ipanema coffees16
 
1.2%
neiva15
 
1.1%
bachue14
 
1.0%
cigrah s.a de c.v.12
 
0.9%
trilladora bonanza - armenia quindioa12
 
0.9%
cadexsa12
 
0.9%
beneficio siembras vision (154)12
 
0.9%
Other values (449)761
56.8%
(Missing)318
23.7%

Length

2022-03-15T09:47:11.944541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
beneficio181
 
6.2%
de105
 
3.6%
ixchel93
 
3.2%
coffee91
 
3.1%
trilladora68
 
2.3%
mill48
 
1.6%
dry46
 
1.6%
la39
 
1.3%
boananza38
 
1.3%
el38
 
1.3%
Other values (796)2186
74.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ICO.Number
Categorical

HIGH CARDINALITY
MISSING

Distinct847
Distinct (%)71.7%
Missing157
Missing (%)11.7%
Memory size91.9 KiB
0
 
77
Taiwan
 
31
2222
 
11
-
 
9
002/1660/0105
 
7
Other values (842)
1047 

Length

Max length40
Median length9
Mean length8.972081218
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique719 ?
Unique (%)60.8%

Sample

1st row2014/2015
2nd row2014/2015
3rd row2014/2015
4th row010/0338
5th row010/0338

Common Values

ValueCountFrequency (%)
077
 
5.8%
Taiwan31
 
2.3%
222211
 
0.8%
-9
 
0.7%
002/1660/01057
 
0.5%
Taiwan台灣7
 
0.5%
002/4177/01507
 
0.5%
002/1660/00806
 
0.4%
16
 
0.4%
unknown6
 
0.4%
Other values (837)1015
75.8%
(Missing)157
 
11.7%

Length

2022-03-15T09:47:12.137986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
077
 
5.9%
taiwan31
 
2.4%
21
 
1.6%
hdoa16
 
1.2%
222211
 
0.8%
none10
 
0.8%
kona9
 
0.7%
18
 
0.6%
002/4177/01507
 
0.5%
taiwan台灣7
 
0.5%
Other values (879)1099
84.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Company
Categorical

HIGH CARDINALITY
MISSING

Distinct281
Distinct (%)24.9%
Missing209
Missing (%)15.6%
Memory size106.1 KiB
unex guatemala, s.a.
 
86
ipanema coffees
 
50
kona pacific farmers cooperative
 
40
racafe & cia s.c.a
 
40
exportadora de cafe condor s.a
 
40
Other values (276)
874 

Length

Max length78
Median length20
Mean length21.09469027
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)12.7%

Sample

1st rowmetad agricultural developmet plc
2nd rowmetad agricultural developmet plc
3rd rowyidnekachew debessa coffee plantation
4th rowmetad agricultural developmet plc
5th rowrichmond investment-coffee department

Common Values

ValueCountFrequency (%)
unex guatemala, s.a.86
 
6.4%
ipanema coffees50
 
3.7%
kona pacific farmers cooperative40
 
3.0%
racafe & cia s.c.a40
 
3.0%
exportadora de cafe condor s.a40
 
3.0%
blossom valley宸嶧國際25
 
1.9%
carcafe ltda25
 
1.9%
nucoffee24
 
1.8%
taiwan coffee laboratory20
 
1.5%
ecomtrading19
 
1.4%
Other values (271)761
56.8%
(Missing)209
 
15.6%

Length

2022-03-15T09:47:12.355403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s.a246
 
6.7%
de215
 
5.9%
coffee185
 
5.0%
guatemala104
 
2.8%
ltd87
 
2.4%
unex86
 
2.3%
exportadora70
 
1.9%
cafe70
 
1.9%
coffees69
 
1.9%
64
 
1.7%
Other values (519)2479
67.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Altitude
Categorical

HIGH CARDINALITY
MISSING

Distinct396
Distinct (%)35.6%
Missing226
Missing (%)16.9%
Memory size86.6 KiB
1100
 
43
1200
 
42
1300
 
32
1400
 
32
4300
 
31
Other values (391)
933 

Length

Max length41
Median length4
Mean length6.355795148
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique251 ?
Unique (%)22.6%

Sample

1st row1950-2200
2nd row1950-2200
3rd row1600 - 1800 m
4th row1800-2200
5th row1950-2200

Common Values

ValueCountFrequency (%)
110043
 
3.2%
120042
 
3.1%
130032
 
2.4%
140032
 
2.4%
430031
 
2.3%
150030
 
2.2%
125030
 
2.2%
170028
 
2.1%
155024
 
1.8%
160023
 
1.7%
Other values (386)798
59.6%
(Missing)226
 
16.9%

Length

2022-03-15T09:47:12.544027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
msnm105
 
6.6%
m60
 
3.7%
120059
 
3.7%
160056
 
3.5%
140052
 
3.2%
110047
 
2.9%
130044
 
2.7%
150042
 
2.6%
180040
 
2.5%
a36
 
2.2%
Other values (329)1061
66.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Region
Categorical

HIGH CARDINALITY
MISSING

Distinct356
Distinct (%)27.8%
Missing59
Missing (%)4.4%
Memory size99.9 KiB
huila
112 
oriente
 
80
south of minas
 
68
kona
 
66
veracruz
 
35
Other values (351)
919 

Length

Max length76
Median length8
Mean length10.92265625
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)15.6%

Sample

1st rowguji-hambela
2nd rowguji-hambela
3rd roworomia
4th rowguji-hambela
5th roworomia

Common Values

ValueCountFrequency (%)
huila112
 
8.4%
oriente80
 
6.0%
south of minas68
 
5.1%
kona66
 
4.9%
veracruz35
 
2.6%
tarrazu19
 
1.4%
comayagua17
 
1.3%
san marcos16
 
1.2%
huehuetenango16
 
1.2%
marcala15
 
1.1%
Other values (346)836
62.4%
(Missing)59
 
4.4%

Length

2022-03-15T09:47:12.728534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
huila114
 
5.3%
oriente89
 
4.1%
minas88
 
4.1%
south73
 
3.4%
of73
 
3.4%
kona66
 
3.1%
de41
 
1.9%
san40
 
1.9%
veracruz36
 
1.7%
chiapas35
 
1.6%
Other values (488)1495
69.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Producer
Categorical

HIGH CARDINALITY
MISSING

Distinct692
Distinct (%)62.5%
Missing232
Missing (%)17.3%
Memory size106.5 KiB
La Plata
 
30
Ipanema Agrícola SA
 
22
Doi Tung Development Project
 
17
VARIOS
 
12
Ipanema Agricola
 
12
Other values (687)
1014 

Length

Max length100
Median length19
Mean length20.55826558
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique538 ?
Unique (%)48.6%

Sample

1st rowMETAD PLC
2nd rowMETAD PLC
3rd rowYidnekachew Dabessa Coffee Plantation
4th rowMETAD PLC
5th rowHVC

Common Values

ValueCountFrequency (%)
La Plata30
 
2.2%
Ipanema Agrícola SA22
 
1.6%
Doi Tung Development Project17
 
1.3%
VARIOS12
 
0.9%
Ipanema Agricola12
 
0.9%
Ipanema Agricola S.A11
 
0.8%
ROBERTO MONTERROSO10
 
0.7%
Reinerio Zepeda9
 
0.7%
LA PLATA9
 
0.7%
AMILCAR LAPOLA9
 
0.7%
Other values (682)966
72.1%
(Missing)232
 
17.3%

Length

2022-03-15T09:47:12.916586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de86
 
2.5%
coffee69
 
2.0%
65
 
1.9%
la60
 
1.7%
s.a52
 
1.5%
ipanema50
 
1.4%
plata41
 
1.2%
agricola37
 
1.1%
ltd36
 
1.0%
sa30
 
0.9%
Other values (1260)2972
85.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Number.of.Bags
Real number (ℝ≥0)

HIGH CORRELATION

Distinct131
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.1829724
Minimum0
Maximum1062
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:13.095147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114
median175
Q3275
95-th percentile320
Maximum1062
Range1062
Interquartile range (IQR)261

Descriptive statistics

Standard deviation129.9871621
Coefficient of variation (CV)0.8430708016
Kurtosis0.221701387
Mean154.1829724
Median Absolute Deviation (MAD)125
Skewness0.3139022021
Sum206451
Variance16896.66231
MonotonicityNot monotonic
2022-03-15T09:47:13.271641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250242
18.1%
275176
13.1%
10108
 
8.1%
195
 
7.1%
32079
 
5.9%
30074
 
5.5%
5042
 
3.1%
10040
 
3.0%
2036
 
2.7%
229
 
2.2%
Other values (121)418
31.2%
ValueCountFrequency (%)
01
 
0.1%
195
7.1%
229
 
2.2%
318
 
1.3%
46
 
0.4%
514
 
1.0%
610
 
0.7%
78
 
0.6%
810
 
0.7%
91
 
0.1%
ValueCountFrequency (%)
10621
 
0.1%
6001
 
0.1%
5502
0.1%
5002
0.1%
4502
0.1%
4403
0.2%
4001
 
0.1%
3801
 
0.1%
3771
 
0.1%
3604
0.3%

Bag.Weight
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size91.1 KiB
1 kg
331 
60 kg
256 
69 kg
200 
70 kg
156 
2 kg
122 
Other values (51)
274 

Length

Max length8
Median length5
Mean length4.700522778
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)1.7%

Sample

1st row60 kg
2nd row60 kg
3rd row1
4th row60 kg
5th row60 kg

Common Values

ValueCountFrequency (%)
1 kg331
24.7%
60 kg256
19.1%
69 kg200
14.9%
70 kg156
11.7%
2 kg122
 
9.1%
100 lbs59
 
4.4%
30 kg29
 
2.2%
5 lbs23
 
1.7%
619
 
1.4%
20 kg14
 
1.0%
Other values (46)130
 
9.7%

Length

2022-03-15T09:47:13.436238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kg1196
45.1%
1347
 
13.1%
60256
 
9.7%
69200
 
7.5%
70156
 
5.9%
2129
 
4.9%
lbs114
 
4.3%
10060
 
2.3%
530
 
1.1%
3029
 
1.1%
Other values (36)134
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

In.Country.Partner
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size124.9 KiB
Specialty Coffee Association
313 
AMECAFE
205 
Almacafé
178 
Asociacion Nacional Del Café
155 
Brazil Specialty Coffee Association
67 
Other values (22)
421 

Length

Max length85
Median length28
Mean length23.08812547
Min length7

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowMETAD Agricultural Development plc
2nd rowMETAD Agricultural Development plc
3rd rowSpecialty Coffee Association
4th rowMETAD Agricultural Development plc
5th rowMETAD Agricultural Development plc

Common Values

ValueCountFrequency (%)
Specialty Coffee Association313
23.4%
AMECAFE205
15.3%
Almacafé178
13.3%
Asociacion Nacional Del Café155
11.6%
Brazil Specialty Coffee Association67
 
5.0%
Instituto Hondureño del Café60
 
4.5%
Blossom Valley International58
 
4.3%
Africa Fine Coffee Association49
 
3.7%
Specialty Coffee Association of Costa Rica42
 
3.1%
NUCOFFEE36
 
2.7%
Other values (17)176
13.1%

Length

2022-03-15T09:47:13.601753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
coffee590
15.3%
association503
13.1%
specialty449
11.7%
café225
 
5.8%
del224
 
5.8%
amecafe205
 
5.3%
almacafé178
 
4.6%
asociacion155
 
4.0%
nacional155
 
4.0%
of68
 
1.8%
Other values (52)1095
28.5%

Most occurring characters

ValueCountFrequency (%)
1
100.0%

Most occurring categories

ValueCountFrequency (%)
Control1
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1
100.0%

Harvest.Year
Categorical

HIGH CORRELATION
MISSING

Distinct46
Distinct (%)3.6%
Missing47
Missing (%)3.5%
Memory size89.8 KiB
2012
354 
2014
233 
2013
181 
2015
129 
2016
124 
Other values (41)
271 

Length

Max length24
Median length4
Mean length4.722136223
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)1.7%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2014
5th row2013

Common Values

ValueCountFrequency (%)
2012354
26.4%
2014233
17.4%
2013181
13.5%
2015129
 
9.6%
2016124
 
9.3%
201770
 
5.2%
2013/201429
 
2.2%
2015/201628
 
2.1%
201126
 
1.9%
2017 / 201819
 
1.4%
Other values (36)99
 
7.4%
(Missing)47
 
3.5%

Length

2022-03-15T09:47:13.747402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012354
25.6%
2014233
16.9%
2013181
13.1%
2016130
 
9.4%
2015129
 
9.3%
201795
 
6.9%
31
 
2.2%
201130
 
2.2%
2013/201429
 
2.1%
2015/201628
 
2.0%
Other values (40)142
10.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Grading.Date
Categorical

HIGH CARDINALITY

Distinct567
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Memory size106.7 KiB
July 11th, 2012
 
25
December 26th, 2013
 
24
June 6th, 2012
 
19
August 30th, 2012
 
18
July 26th, 2012
 
15
Other values (562)
1238 

Length

Max length20
Median length16
Mean length16.59148618
Min length13

Characters and Unicode

Total characters2
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique283 ?
Unique (%)21.1%

Sample

1st rowApril 4th, 2015
2nd rowApril 4th, 2015
3rd rowMay 31st, 2010
4th rowMarch 26th, 2015
5th rowApril 4th, 2015

Common Values

ValueCountFrequency (%)
July 11th, 201225
 
1.9%
December 26th, 201324
 
1.8%
June 6th, 201219
 
1.4%
August 30th, 201218
 
1.3%
July 26th, 201215
 
1.1%
October 8th, 201513
 
1.0%
March 29th, 201313
 
1.0%
September 27th, 201213
 
1.0%
June 17th, 201012
 
0.9%
October 20th, 201711
 
0.8%
Other values (557)1176
87.8%

Length

2022-03-15T09:47:13.879010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012316
 
7.9%
2014262
 
6.5%
2015197
 
4.9%
june162
 
4.0%
2013155
 
3.9%
2017143
 
3.6%
july130
 
3.2%
april129
 
3.2%
may128
 
3.2%
2016127
 
3.2%
Other values (42)2268
56.5%

Most occurring characters

ValueCountFrequency (%)
2
100.0%

Most occurring categories

ValueCountFrequency (%)
Control2
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2
100.0%

Owner.1
Categorical

HIGH CARDINALITY

Distinct319
Distinct (%)23.9%
Missing7
Missing (%)0.5%
Memory size117.1 KiB
Juan Luis Alvarado Romero
155 
Racafe & Cia S.C.A
 
60
Exportadora de Cafe Condor S.A
 
54
Kona Pacific Farmers Cooperative
 
52
Ipanema Coffees
 
50
Other values (314)
961 

Length

Max length50
Median length22
Mean length21.21246246
Min length3

Characters and Unicode

Total characters2
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)13.2%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowGrounds for Health Admin
4th rowYidnekachew Dabessa
5th rowmetad plc

Common Values

ValueCountFrequency (%)
Juan Luis Alvarado Romero155
 
11.6%
Racafe & Cia S.C.A60
 
4.5%
Exportadora de Cafe Condor S.A54
 
4.0%
Kona Pacific Farmers Cooperative52
 
3.9%
Ipanema Coffees50
 
3.7%
CQI Taiwan ICP CQI台灣合作夥伴46
 
3.4%
Lin, Che-Hao Krude 林哲豪29
 
2.2%
NUCOFFEE29
 
2.2%
CARCAFE LTDA CI27
 
2.0%
The Coffee Source Inc.23
 
1.7%
Other values (309)807
60.3%

Length

2022-03-15T09:47:14.039665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
luis169
 
3.9%
juan160
 
3.7%
alvarado155
 
3.6%
romero155
 
3.6%
de114
 
2.6%
s.a100
 
2.3%
coffee87
 
2.0%
cafe74
 
1.7%
exportadora70
 
1.6%
coffees67
 
1.6%
Other values (656)3168
73.4%

Most occurring characters

ValueCountFrequency (%)
2
100.0%

Most occurring categories

ValueCountFrequency (%)
Control2
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2
100.0%

Variety
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct29
Distinct (%)2.6%
Missing226
Missing (%)16.9%
Memory size87.1 KiB
Caturra
256 
Bourbon
226 
Typica
211 
Other
110 
Catuai
74 
Other values (24)
236 

Length

Max length21
Median length7
Mean length7.017070979
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.5%

Sample

1st rowOther
2nd rowBourbon
3rd rowOther
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
Caturra256
19.1%
Bourbon226
16.9%
Typica211
15.8%
Other110
8.2%
Catuai74
 
5.5%
Hawaiian Kona44
 
3.3%
Yellow Bourbon35
 
2.6%
Mundo Novo33
 
2.5%
Catimor20
 
1.5%
SL1417
 
1.3%
Other values (19)87
 
6.5%
(Missing)226
16.9%

Length

2022-03-15T09:47:14.192259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bourbon261
21.1%
caturra256
20.7%
typica211
17.1%
other110
8.9%
catuai74
 
6.0%
hawaiian44
 
3.6%
kona44
 
3.6%
yellow35
 
2.8%
mundo33
 
2.7%
novo33
 
2.7%
Other values (25)134
10.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Processing.Method
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.4%
Missing170
Missing (%)12.7%
Memory size95.5 KiB
Washed / Wet
815 
Natural / Dry
258 
Semi-washed / Semi-pulped
 
56
Other
 
26
Pulped natural / honey
 
14

Length

Max length25
Median length12
Mean length12.8075278
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWashed / Wet
2nd rowWashed / Wet
3rd rowNatural / Dry
4th rowWashed / Wet
5th rowNatural / Dry

Common Values

ValueCountFrequency (%)
Washed / Wet815
60.9%
Natural / Dry258
 
19.3%
Semi-washed / Semi-pulped56
 
4.2%
Other26
 
1.9%
Pulped natural / honey14
 
1.0%
(Missing)170
 
12.7%

Length

2022-03-15T09:47:14.335386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T09:47:14.949626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1143
32.9%
washed815
23.5%
wet815
23.5%
natural272
 
7.8%
dry258
 
7.4%
semi-washed56
 
1.6%
semi-pulped56
 
1.6%
other26
 
0.7%
pulped14
 
0.4%
honey14
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Aroma
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.566706497
Minimum0
Maximum8.75
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:15.139087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.08
Q17.42
median7.58
Q37.75
95-th percentile8.08
Maximum8.75
Range8.75
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.377559997
Coefficient of variation (CV)0.0498975343
Kurtosis121.4173128
Mean7.566706497
Median Absolute Deviation (MAD)0.17
Skewness-6.245392604
Sum10131.82
Variance0.1425515513
MonotonicityNot monotonic
2022-03-15T09:47:15.356504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7.67179
13.4%
7.5165
12.3%
7.58152
11.4%
7.75125
9.3%
7.42122
9.1%
7.83103
7.7%
7.3398
7.3%
7.2578
 
5.8%
7.9259
 
4.4%
848
 
3.6%
Other values (23)210
15.7%
ValueCountFrequency (%)
01
 
0.1%
5.081
 
0.1%
6.171
 
0.1%
6.331
 
0.1%
6.421
 
0.1%
6.52
 
0.1%
6.673
 
0.2%
6.757
0.5%
6.839
0.7%
6.9214
1.0%
ValueCountFrequency (%)
8.751
 
0.1%
8.672
 
0.1%
8.581
 
0.1%
8.53
 
0.2%
8.429
 
0.7%
8.337
 
0.5%
8.259
 
0.7%
8.1720
1.5%
8.0820
1.5%
848
3.6%

Flavor
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct35
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.520425691
Minimum0
Maximum8.83
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:15.498202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.92
Q17.33
median7.58
Q37.75
95-th percentile8
Maximum8.83
Range8.83
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.398442087
Coefficient of variation (CV)0.05298132092
Kurtosis94.7532297
Mean7.520425691
Median Absolute Deviation (MAD)0.17
Skewness-5.195526489
Sum10069.85
Variance0.1587560967
MonotonicityNot monotonic
2022-03-15T09:47:15.648798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7.5166
12.4%
7.58166
12.4%
7.67148
11.1%
7.75126
9.4%
7.42116
8.7%
7.33111
8.3%
7.8389
 
6.6%
7.2564
 
4.8%
7.1756
 
4.2%
7.9245
 
3.4%
Other values (25)252
18.8%
ValueCountFrequency (%)
01
 
0.1%
6.081
 
0.1%
6.172
 
0.1%
6.333
 
0.2%
6.421
 
0.1%
6.59
0.7%
6.585
 
0.4%
6.675
 
0.4%
6.7510
0.7%
6.8317
1.3%
ValueCountFrequency (%)
8.831
 
0.1%
8.674
 
0.3%
8.582
 
0.1%
8.55
 
0.4%
8.425
 
0.4%
8.335
 
0.4%
8.257
 
0.5%
8.1718
1.3%
8.0814
 
1.0%
841
3.1%

Aftertaste
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct35
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.401082898
Minimum0
Maximum8.67
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:15.796364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.83
Q17.25
median7.42
Q37.58
95-th percentile7.92
Maximum8.67
Range8.67
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.404463447
Coefficient of variation (CV)0.05464922534
Kurtosis83.55698524
Mean7.401082898
Median Absolute Deviation (MAD)0.17
Skewness-4.79095828
Sum9910.05
Variance0.16359068
MonotonicityNot monotonic
2022-03-15T09:47:15.963941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7.5164
12.2%
7.33153
11.4%
7.42129
9.6%
7.58126
9.4%
7.25104
7.8%
7.67102
 
7.6%
7.1791
 
6.8%
7.7587
 
6.5%
7.8365
 
4.9%
762
 
4.6%
Other values (25)256
19.1%
ValueCountFrequency (%)
01
 
0.1%
6.178
 
0.6%
6.251
 
0.1%
6.336
 
0.4%
6.424
 
0.3%
6.57
 
0.5%
6.586
 
0.4%
6.6714
 
1.0%
6.7510
 
0.7%
6.8336
2.7%
ValueCountFrequency (%)
8.671
 
0.1%
8.582
 
0.1%
8.54
 
0.3%
8.423
 
0.2%
8.332
 
0.1%
8.254
 
0.3%
8.177
 
0.5%
8.087
 
0.5%
827
2.0%
7.9222
1.6%

Acidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.535705751
Minimum0
Maximum8.75
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:16.113051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q17.33
median7.58
Q37.75
95-th percentile8.08
Maximum8.75
Range8.75
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.3798266933
Coefficient of variation (CV)0.05040359933
Kurtosis116.1394574
Mean7.535705751
Median Absolute Deviation (MAD)0.17
Skewness-5.943258891
Sum10090.31
Variance0.1442683169
MonotonicityNot monotonic
2022-03-15T09:47:16.247731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7.5162
12.1%
7.58154
11.5%
7.67146
10.9%
7.42129
9.6%
7.75126
9.4%
7.33111
8.3%
7.2586
 
6.4%
7.8378
 
5.8%
7.1774
 
5.5%
850
 
3.7%
Other values (21)223
16.7%
ValueCountFrequency (%)
01
 
0.1%
5.251
 
0.1%
6.081
 
0.1%
6.251
 
0.1%
6.51
 
0.1%
6.675
 
0.4%
6.756
 
0.4%
6.8312
 
0.9%
6.9210
 
0.7%
732
2.4%
ValueCountFrequency (%)
8.751
 
0.1%
8.581
 
0.1%
8.57
 
0.5%
8.426
 
0.4%
8.339
 
0.7%
8.256
 
0.4%
8.1714
 
1.0%
8.0825
1.9%
850
3.7%
7.9247
3.5%

Body
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.517498133
Minimum0
Maximum8.58
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:16.388314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.08
Q17.33
median7.5
Q37.67
95-th percentile8
Maximum8.58
Range8.58
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.3700639451
Coefficient of variation (CV)0.04922700857
Kurtosis129.9339034
Mean7.517498133
Median Absolute Deviation (MAD)0.17
Skewness-6.796184885
Sum10065.93
Variance0.1369473234
MonotonicityNot monotonic
2022-03-15T09:47:16.534925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7.5201
15.0%
7.67154
11.5%
7.58138
10.3%
7.33131
9.8%
7.42127
9.5%
7.75111
8.3%
7.2587
6.5%
7.8385
6.3%
7.1768
 
5.1%
7.9252
 
3.9%
Other values (23)185
13.8%
ValueCountFrequency (%)
01
 
0.1%
5.081
 
0.1%
5.171
 
0.1%
5.251
 
0.1%
6.332
0.1%
6.421
 
0.1%
6.51
 
0.1%
6.672
0.1%
6.754
0.3%
6.834
0.3%
ValueCountFrequency (%)
8.581
 
0.1%
8.53
 
0.2%
8.423
 
0.2%
8.336
 
0.4%
8.257
 
0.5%
8.177
 
0.5%
8.0821
 
1.6%
834
 
2.5%
7.9252
3.9%
7.8385
6.3%

Balance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.518013443
Minimum0
Maximum8.75
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:16.679576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.92
Q17.33
median7.5
Q37.75
95-th percentile8.08
Maximum8.75
Range8.75
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.4089432915
Coefficient of variation (CV)0.0543951264
Kurtosis85.63351803
Mean7.518013443
Median Absolute Deviation (MAD)0.17
Skewness-4.783035801
Sum10066.62
Variance0.1672346157
MonotonicityNot monotonic
2022-03-15T09:47:16.811183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7.5176
13.1%
7.67148
11.1%
7.58131
9.8%
7.42121
9.0%
7.75107
 
8.0%
7.83101
 
7.5%
7.3399
 
7.4%
7.1772
 
5.4%
7.2564
 
4.8%
747
 
3.5%
Other values (23)273
20.4%
ValueCountFrequency (%)
01
 
0.1%
5.251
 
0.1%
6.081
 
0.1%
6.173
0.2%
6.331
 
0.1%
6.421
 
0.1%
6.52
 
0.1%
6.583
0.2%
6.674
0.3%
6.757
0.5%
ValueCountFrequency (%)
8.752
 
0.1%
8.587
 
0.5%
8.57
 
0.5%
8.427
 
0.5%
8.337
 
0.5%
8.258
 
0.6%
8.1717
 
1.3%
8.0816
 
1.2%
846
3.4%
7.9242
3.1%

Uniformity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.834876774
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:16.934852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.67
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5545906088
Coefficient of variation (CV)0.05639019396
Kurtosis85.35978352
Mean9.834876774
Median Absolute Deviation (MAD)0
Skewness-6.966567106
Sum13168.9
Variance0.3075707434
MonotonicityNot monotonic
2022-03-15T09:47:17.048588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
101152
86.0%
9.33116
 
8.7%
8.6731
 
2.3%
825
 
1.9%
6.677
 
0.5%
63
 
0.2%
7.332
 
0.1%
9.51
 
0.1%
91
 
0.1%
01
 
0.1%
ValueCountFrequency (%)
01
 
0.1%
63
 
0.2%
6.677
 
0.5%
7.332
 
0.1%
825
 
1.9%
8.6731
 
2.3%
91
 
0.1%
9.33116
 
8.7%
9.51
 
0.1%
101152
86.0%
ValueCountFrequency (%)
101152
86.0%
9.51
 
0.1%
9.33116
 
8.7%
91
 
0.1%
8.6731
 
2.3%
825
 
1.9%
7.332
 
0.1%
6.677
 
0.5%
63
 
0.2%
01
 
0.1%

Clean.Cup
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.83510829
Minimum0
Maximum10
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:17.166274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.33
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7639459063
Coefficient of variation (CV)0.07767539348
Kurtosis70.51362253
Mean9.83510829
Median Absolute Deviation (MAD)0
Skewness-7.44352063
Sum13169.21
Variance0.5836133478
MonotonicityNot monotonic
2022-03-15T09:47:17.285915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
101219
91.0%
9.3361
 
4.6%
8.6716
 
1.2%
6.6713
 
1.0%
813
 
1.0%
66
 
0.4%
5.333
 
0.2%
7.333
 
0.2%
2.672
 
0.1%
02
 
0.1%
ValueCountFrequency (%)
02
 
0.1%
1.331
 
0.1%
2.672
 
0.1%
5.333
 
0.2%
66
 
0.4%
6.6713
 
1.0%
7.333
 
0.2%
813
 
1.0%
8.6716
 
1.2%
9.3361
4.6%
ValueCountFrequency (%)
101219
91.0%
9.3361
 
4.6%
8.6716
 
1.2%
813
 
1.0%
7.333
 
0.2%
6.6713
 
1.0%
66
 
0.4%
5.333
 
0.2%
2.672
 
0.1%
1.331
 
0.1%

Sweetness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.856691561
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:17.406631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.33
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6161019777
Coefficient of variation (CV)0.06250596094
Kurtosis85.32969061
Mean9.856691561
Median Absolute Deviation (MAD)0
Skewness-7.559007962
Sum13198.11
Variance0.3795816469
MonotonicityNot monotonic
2022-03-15T09:47:17.537460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
101218
91.0%
9.3361
 
4.6%
8.6712
 
0.9%
811
 
0.8%
6.678
 
0.6%
7.757
 
0.5%
7.585
 
0.4%
63
 
0.2%
7.423
 
0.2%
7.922
 
0.1%
Other values (7)9
 
0.7%
ValueCountFrequency (%)
01
 
0.1%
1.331
 
0.1%
63
 
0.2%
6.678
0.6%
7.081
 
0.1%
7.423
 
0.2%
7.51
 
0.1%
7.585
0.4%
7.672
 
0.1%
7.757
0.5%
ValueCountFrequency (%)
101218
91.0%
9.3361
 
4.6%
8.6712
 
0.9%
8.421
 
0.1%
811
 
0.8%
7.922
 
0.1%
7.832
 
0.1%
7.757
 
0.5%
7.672
 
0.1%
7.585
 
0.4%

Cupper.Points
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct42
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.503375653
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:17.684114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.83
Q17.25
median7.5
Q37.75
95-th percentile8.08
Maximum10
Range10
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.4734642722
Coefficient of variation (CV)0.06310016906
Kurtosis49.71213566
Mean7.503375653
Median Absolute Deviation (MAD)0.25
Skewness-2.815594635
Sum10047.02
Variance0.2241684171
MonotonicityNot monotonic
2022-03-15T09:47:17.842657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
7.5154
11.5%
7.58138
10.3%
7.33115
 
8.6%
7.67115
 
8.6%
7.42104
 
7.8%
7.8387
 
6.5%
7.7587
 
6.5%
7.2585
 
6.3%
7.1764
 
4.8%
7.9255
 
4.1%
Other values (32)335
25.0%
ValueCountFrequency (%)
01
 
0.1%
5.171
 
0.1%
5.251
 
0.1%
5.421
 
0.1%
61
 
0.1%
6.173
0.2%
6.251
 
0.1%
6.333
0.2%
6.425
0.4%
6.56
0.4%
ValueCountFrequency (%)
104
0.3%
9.251
 
0.1%
91
 
0.1%
8.831
 
0.1%
8.751
 
0.1%
8.672
 
0.1%
8.586
0.4%
8.58
0.6%
8.426
0.4%
8.339
0.7%

Total.Cup.Points
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct180
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.08985063
Minimum0
Maximum90.58
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:18.013237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.83
Q181.08
median82.5
Q383.67
95-th percentile85.5
Maximum90.58
Range90.58
Interquartile range (IQR)2.59

Descriptive statistics

Standard deviation3.500575457
Coefficient of variation (CV)0.04264321874
Kurtosis228.0755525
Mean82.08985063
Median Absolute Deviation (MAD)1.25
Skewness-10.44216733
Sum109918.31
Variance12.25402853
MonotonicityNot monotonic
2022-03-15T09:47:18.172777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8339
 
2.9%
83.1738
 
2.8%
82.4232
 
2.4%
82.7530
 
2.2%
82.3329
 
2.2%
82.6726
 
1.9%
81.526
 
1.9%
81.8326
 
1.9%
82.9226
 
1.9%
83.2525
 
1.9%
Other values (170)1042
77.8%
ValueCountFrequency (%)
01
0.1%
59.831
0.1%
63.081
0.1%
67.921
0.1%
68.331
0.1%
69.172
0.1%
69.331
0.1%
70.671
0.1%
70.751
0.1%
711
0.1%
ValueCountFrequency (%)
90.581
0.1%
89.921
0.1%
89.751
0.1%
891
0.1%
88.832
0.1%
88.751
0.1%
88.671
0.1%
88.421
0.1%
88.251
0.1%
88.081
0.1%

Moisture
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0883793876
Minimum0
Maximum0.28
Zeros264
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:18.311402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median0.11
Q30.12
95-th percentile0.13
Maximum0.28
Range0.28
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.04828743714
Coefficient of variation (CV)0.5463653737
Kurtosis-0.1700121692
Mean0.0883793876
Median Absolute Deviation (MAD)0.01
Skewness-0.9928607
Sum118.34
Variance0.002331676585
MonotonicityNot monotonic
2022-03-15T09:47:18.439063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.11383
28.6%
0.12294
22.0%
0264
19.7%
0.1182
13.6%
0.1376
 
5.7%
0.0927
 
2.0%
0.1423
 
1.7%
0.0816
 
1.2%
0.0115
 
1.1%
0.158
 
0.6%
Other values (13)51
 
3.8%
ValueCountFrequency (%)
0264
19.7%
0.0115
 
1.1%
0.027
 
0.5%
0.034
 
0.3%
0.044
 
0.3%
0.058
 
0.6%
0.067
 
0.5%
0.075
 
0.4%
0.0816
 
1.2%
0.0927
 
2.0%
ValueCountFrequency (%)
0.281
 
0.1%
0.221
 
0.1%
0.211
 
0.1%
0.23
 
0.2%
0.182
 
0.1%
0.173
 
0.2%
0.165
 
0.4%
0.158
 
0.6%
0.1423
 
1.7%
0.1376
5.7%

Category.One.Defects
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4794622853
Minimum0
Maximum63
Zeros1137
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:18.592691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum63
Range63
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.549683472
Coefficient of variation (CV)5.317797772
Kurtosis308.7155768
Mean0.4794622853
Median Absolute Deviation (MAD)0
Skewness15.06148967
Sum642
Variance6.500885809
MonotonicityNot monotonic
2022-03-15T09:47:18.710371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
01137
84.9%
1101
 
7.5%
238
 
2.8%
318
 
1.3%
416
 
1.2%
59
 
0.7%
104
 
0.3%
63
 
0.2%
73
 
0.2%
312
 
0.1%
Other values (8)8
 
0.6%
ValueCountFrequency (%)
01137
84.9%
1101
 
7.5%
238
 
2.8%
318
 
1.3%
416
 
1.2%
59
 
0.7%
63
 
0.2%
73
 
0.2%
81
 
0.1%
91
 
0.1%
ValueCountFrequency (%)
631
 
0.1%
312
0.1%
231
 
0.1%
201
 
0.1%
151
 
0.1%
121
 
0.1%
111
 
0.1%
104
0.3%
91
 
0.1%
81
 
0.1%

Quakers
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.1733931241
Minimum0
Maximum11
Zeros1244
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:18.837992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8321210408
Coefficient of variation (CV)4.799042899
Kurtosis58.33424418
Mean0.1733931241
Median Absolute Deviation (MAD)0
Skewness6.936339775
Sum232
Variance0.6924254265
MonotonicityNot monotonic
2022-03-15T09:47:18.945742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
01244
92.9%
139
 
2.9%
230
 
2.2%
45
 
0.4%
55
 
0.4%
35
 
0.4%
64
 
0.3%
73
 
0.2%
111
 
0.1%
91
 
0.1%
ValueCountFrequency (%)
01244
92.9%
139
 
2.9%
230
 
2.2%
35
 
0.4%
45
 
0.4%
55
 
0.4%
64
 
0.3%
73
 
0.2%
81
 
0.1%
91
 
0.1%
ValueCountFrequency (%)
111
 
0.1%
91
 
0.1%
81
 
0.1%
73
 
0.2%
64
 
0.3%
55
 
0.4%
45
 
0.4%
35
 
0.4%
230
2.2%
139
2.9%

Color
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.4%
Missing218
Missing (%)16.3%
Memory size86.3 KiB
Green
870 
Bluish-Green
114 
Blue-Green
 
85
None
 
52

Length

Max length12
Median length5
Mean length6.044603033
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreen
2nd rowGreen
3rd rowGreen
4th rowGreen
5th rowBluish-Green

Common Values

ValueCountFrequency (%)
Green870
65.0%
Bluish-Green114
 
8.5%
Blue-Green85
 
6.3%
None52
 
3.9%
(Missing)218
 
16.3%

Length

2022-03-15T09:47:19.082339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T09:47:19.172184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
green870
77.6%
bluish-green114
 
10.2%
blue-green85
 
7.6%
none52
 
4.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Category.Two.Defects
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct38
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.556385362
Minimum0
Maximum55
Zeros373
Zeros (%)27.9%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:19.283931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile13
Maximum55
Range55
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.312540717
Coefficient of variation (CV)1.493803448
Kurtosis20.08268531
Mean3.556385362
Median Absolute Deviation (MAD)2
Skewness3.667136229
Sum4762
Variance28.22308887
MonotonicityNot monotonic
2022-03-15T09:47:19.420564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0373
27.9%
1207
15.5%
2182
13.6%
3134
 
10.0%
4118
 
8.8%
573
 
5.5%
644
 
3.3%
741
 
3.1%
829
 
2.2%
923
 
1.7%
Other values (28)115
 
8.6%
ValueCountFrequency (%)
0373
27.9%
1207
15.5%
2182
13.6%
3134
 
10.0%
4118
 
8.8%
573
 
5.5%
644
 
3.3%
741
 
3.1%
829
 
2.2%
923
 
1.7%
ValueCountFrequency (%)
551
0.1%
471
0.1%
451
0.1%
401
0.1%
381
0.1%
341
0.1%
321
0.1%
311
0.1%
302
0.1%
292
0.1%

Expiration
Categorical

HIGH CARDINALITY

Distinct566
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Memory size106.7 KiB
July 11th, 2013
 
25
December 26th, 2014
 
25
June 6th, 2013
 
19
August 30th, 2013
 
18
July 26th, 2013
 
15
Other values (561)
1237 

Length

Max length20
Median length16
Mean length16.59297984
Min length13

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique282 ?
Unique (%)21.1%

Sample

1st rowApril 3rd, 2016
2nd rowApril 3rd, 2016
3rd rowMay 31st, 2011
4th rowMarch 25th, 2016
5th rowApril 3rd, 2016

Common Values

ValueCountFrequency (%)
July 11th, 201325
 
1.9%
December 26th, 201425
 
1.9%
June 6th, 201319
 
1.4%
August 30th, 201318
 
1.3%
July 26th, 201315
 
1.1%
October 7th, 201613
 
1.0%
March 29th, 201413
 
1.0%
September 27th, 201313
 
1.0%
June 17th, 201112
 
0.9%
October 20th, 201811
 
0.8%
Other values (556)1175
87.8%

Length

2022-03-15T09:47:19.579431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013316
 
7.9%
2015262
 
6.5%
2016197
 
4.9%
june162
 
4.0%
2014155
 
3.9%
2018143
 
3.6%
july130
 
3.2%
april129
 
3.2%
may128
 
3.2%
2017127
 
3.2%
Other values (42)2268
56.5%

Most occurring characters

ValueCountFrequency (%)
1
100.0%

Most occurring categories

ValueCountFrequency (%)
Control1
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1
100.0%

Certification.Body
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size125.0 KiB
Specialty Coffee Association
313 
AMECAFE
205 
Almacafé
178 
Asociacion Nacional Del Café
155 
Brazil Specialty Coffee Association
67 
Other values (21)
421 

Length

Max length85
Median length28
Mean length23.10455564
Min length7

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowMETAD Agricultural Development plc
2nd rowMETAD Agricultural Development plc
3rd rowSpecialty Coffee Association
4th rowMETAD Agricultural Development plc
5th rowMETAD Agricultural Development plc

Common Values

ValueCountFrequency (%)
Specialty Coffee Association313
23.4%
AMECAFE205
15.3%
Almacafé178
13.3%
Asociacion Nacional Del Café155
11.6%
Brazil Specialty Coffee Association67
 
5.0%
Instituto Hondureño del Café60
 
4.5%
Blossom Valley International58
 
4.3%
Africa Fine Coffee Association49
 
3.7%
Specialty Coffee Association of Costa Rica43
 
3.2%
NUCOFFEE36
 
2.7%
Other values (16)175
13.1%

Length

2022-03-15T09:47:19.728032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
coffee590
15.3%
association504
13.1%
specialty449
11.7%
café225
 
5.8%
del224
 
5.8%
amecafe205
 
5.3%
almacafé178
 
4.6%
asociacion155
 
4.0%
nacional155
 
4.0%
of69
 
1.8%
Other values (51)1096
28.5%

Most occurring characters

ValueCountFrequency (%)
1
100.0%

Most occurring categories

ValueCountFrequency (%)
Control1
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1
100.0%

Certification.Address
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size137.3 KiB
36d0d00a3724338ba7937c52a378d085f2172daa
293 
59e396ad6e22a1c22b248f958e1da2bd8af85272
204 
e493c36c2d076bf273064f7ac23ad562af257a25
178 
b1f20fe3a819fd6b2ee0eb8fdc3da256604f1e53
155 
3297cfa4c538e3dd03f72cc4082c54f7999e1f9d
67 
Other values (27)
442 

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.4%

Sample

1st row309fcf77415a3661ae83e027f7e5f05dad786e44
2nd row309fcf77415a3661ae83e027f7e5f05dad786e44
3rd row36d0d00a3724338ba7937c52a378d085f2172daa
4th row309fcf77415a3661ae83e027f7e5f05dad786e44
5th row309fcf77415a3661ae83e027f7e5f05dad786e44

Common Values

ValueCountFrequency (%)
36d0d00a3724338ba7937c52a378d085f2172daa293
21.9%
59e396ad6e22a1c22b248f958e1da2bd8af85272204
15.2%
e493c36c2d076bf273064f7ac23ad562af257a25178
13.3%
b1f20fe3a819fd6b2ee0eb8fdc3da256604f1e53155
11.6%
3297cfa4c538e3dd03f72cc4082c54f7999e1f9d67
 
5.0%
b4660a57e9f8cc613ae5b8f02bfce8634c763ab460
 
4.5%
fc45352eee499d8470cf94c9827922fb745bf81558
 
4.3%
073285c0d45e2f5539012d969937e529564fa6fe48
 
3.6%
8e0b118f3cf3121ab27c5387deacdb7d4d2a60b142
 
3.1%
567f200bcc17a90070cb952647bf88141ad9c80c36
 
2.7%
Other values (22)198
14.8%

Length

2022-03-15T09:47:19.864709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
36d0d00a3724338ba7937c52a378d085f2172daa293
21.9%
59e396ad6e22a1c22b248f958e1da2bd8af85272204
15.2%
e493c36c2d076bf273064f7ac23ad562af257a25178
13.3%
b1f20fe3a819fd6b2ee0eb8fdc3da256604f1e53155
11.6%
3297cfa4c538e3dd03f72cc4082c54f7999e1f9d67
 
5.0%
b4660a57e9f8cc613ae5b8f02bfce8634c763ab460
 
4.5%
fc45352eee499d8470cf94c9827922fb745bf81558
 
4.3%
073285c0d45e2f5539012d969937e529564fa6fe48
 
3.6%
8e0b118f3cf3121ab27c5387deacdb7d4d2a60b142
 
3.1%
567f200bcc17a90070cb952647bf88141ad9c80c36
 
2.7%
Other values (22)198
14.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Certification.Contact
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size137.3 KiB
0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660
295 
0eb4ee5b3f47b20b049548a2fd1e7d4a2b70d0a7
204 
70d3c0c26f89e00fdae6fb39ff54f0d2eb1c38ab
178 
724f04ad10ed31dbb9d260f0dfd221ba48be8a95
155 
8900f0bf1d0b2bafe6807a73562c7677d57eb980
67 
Other values (24)
440 

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row19fef5a731de2db57d16da10287413f5f99bc2dd
2nd row19fef5a731de2db57d16da10287413f5f99bc2dd
3rd row0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660
4th row19fef5a731de2db57d16da10287413f5f99bc2dd
5th row19fef5a731de2db57d16da10287413f5f99bc2dd

Common Values

ValueCountFrequency (%)
0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660295
22.0%
0eb4ee5b3f47b20b049548a2fd1e7d4a2b70d0a7204
15.2%
70d3c0c26f89e00fdae6fb39ff54f0d2eb1c38ab178
13.3%
724f04ad10ed31dbb9d260f0dfd221ba48be8a95155
11.6%
8900f0bf1d0b2bafe6807a73562c7677d57eb98067
 
5.0%
7f521ca403540f81ec99daec7da19c278839388060
 
4.5%
de73fc9412358b523d3a641501e542f31d2668b058
 
4.3%
c4ab13415cdd69376a93780c0166e7b1a10481ea49
 
3.7%
5eb2b7129d9714c43825e44dc3bca9423de209e943
 
3.2%
aa2ff513ffb9c844462a1fb07c599bce7f3bb53d36
 
2.7%
Other values (19)194
14.5%

Length

2022-03-15T09:47:19.986343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660295
22.0%
0eb4ee5b3f47b20b049548a2fd1e7d4a2b70d0a7204
15.2%
70d3c0c26f89e00fdae6fb39ff54f0d2eb1c38ab178
13.3%
724f04ad10ed31dbb9d260f0dfd221ba48be8a95155
11.6%
8900f0bf1d0b2bafe6807a73562c7677d57eb98067
 
5.0%
7f521ca403540f81ec99daec7da19c278839388060
 
4.5%
de73fc9412358b523d3a641501e542f31d2668b058
 
4.3%
c4ab13415cdd69376a93780c0166e7b1a10481ea49
 
3.7%
5eb2b7129d9714c43825e44dc3bca9423de209e943
 
3.2%
aa2ff513ffb9c844462a1fb07c599bce7f3bb53d36
 
2.7%
Other values (19)194
14.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

unit_of_measurement
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size86.5 KiB
m
1157 
ft
182 

Length

Max length2
Median length1
Mean length1.13592233
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowm
2nd rowm
3rd rowm
4th rowm
5th rowm

Common Values

ValueCountFrequency (%)
m1157
86.4%
ft182
 
13.6%

Length

2022-03-15T09:47:20.116035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T09:47:20.201478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
m1157
86.4%
ft182
 
13.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

altitude_low_meters
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct198
Distinct (%)17.9%
Missing230
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean1750.713315
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:20.302342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile442
Q11100
median1310.64
Q31600
95-th percentile1850
Maximum190164
Range190163
Interquartile range (IQR)500

Descriptive statistics

Standard deviation8669.440545
Coefficient of variation (CV)4.951947569
Kurtosis423.9278958
Mean1750.713315
Median Absolute Deviation (MAD)239.36
Skewness20.3231837
Sum1941541.066
Variance75159199.36
MonotonicityNot monotonic
2022-03-15T09:47:20.470349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120082
 
6.1%
160066
 
4.9%
140059
 
4.4%
110055
 
4.1%
150054
 
4.0%
130048
 
3.6%
180041
 
3.1%
125038
 
2.8%
170036
 
2.7%
100034
 
2.5%
Other values (188)596
44.5%
(Missing)230
 
17.2%
ValueCountFrequency (%)
114
1.0%
123
 
0.2%
132
 
0.1%
401
 
0.1%
501
 
0.1%
1002
 
0.1%
1101
 
0.1%
1251
 
0.1%
1502
 
0.1%
157.88643
 
0.2%
ValueCountFrequency (%)
1901642
0.1%
1100001
 
0.1%
110001
 
0.1%
42871
 
0.1%
40011
 
0.1%
38451
 
0.1%
38251
 
0.1%
38001
 
0.1%
35001
 
0.1%
32803
0.2%

altitude_high_meters
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct198
Distinct (%)17.9%
Missing230
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean1799.347775
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:20.647912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile442
Q11100
median1350
Q31650
95-th percentile1950
Maximum190164
Range190163
Interquartile range (IQR)550

Descriptive statistics

Standard deviation8668.805771
Coefficient of variation (CV)4.817748904
Kurtosis423.595361
Mean1799.347775
Median Absolute Deviation (MAD)250
Skewness20.31093398
Sum1995476.682
Variance75148193.5
MonotonicityNot monotonic
2022-03-15T09:47:20.813941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120066
 
4.9%
140063
 
4.7%
110054
 
4.0%
150051
 
3.8%
130046
 
3.4%
180044
 
3.3%
170042
 
3.1%
125039
 
2.9%
160035
 
2.6%
100035
 
2.6%
Other values (188)634
47.3%
(Missing)230
 
17.2%
ValueCountFrequency (%)
112
0.9%
123
 
0.2%
132
 
0.1%
401
 
0.1%
501
 
0.1%
1001
 
0.1%
1101
 
0.1%
1251
 
0.1%
1502
 
0.1%
157.88643
 
0.2%
ValueCountFrequency (%)
1901642
0.1%
1100001
0.1%
110001
0.1%
59001
0.1%
42871
0.1%
40011
0.1%
38451
0.1%
38251
0.1%
38001
0.1%
35001
0.1%

altitude_mean_meters
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct211
Distinct (%)19.0%
Missing230
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean1775.030545
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2022-03-15T09:47:20.985991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile442
Q11100
median1310.64
Q31600
95-th percentile1892
Maximum190164
Range190163
Interquartile range (IQR)500

Descriptive statistics

Standard deviation8668.62608
Coefficient of variation (CV)4.883648963
Kurtosis423.8594836
Mean1775.030545
Median Absolute Deviation (MAD)251.64
Skewness20.32052739
Sum1968508.874
Variance75145078.11
MonotonicityNot monotonic
2022-03-15T09:47:21.162567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120067
 
5.0%
110053
 
4.0%
140052
 
3.9%
130050
 
3.7%
150044
 
3.3%
125040
 
3.0%
100036
 
2.7%
160036
 
2.7%
170036
 
2.7%
175034
 
2.5%
Other values (201)661
49.4%
(Missing)230
 
17.2%
ValueCountFrequency (%)
112
0.9%
123
 
0.2%
132
 
0.1%
401
 
0.1%
501
 
0.1%
1001
 
0.1%
1101
 
0.1%
1251
 
0.1%
1502
 
0.1%
157.88643
 
0.2%
ValueCountFrequency (%)
1901642
0.1%
1100001
0.1%
110001
0.1%
42871
0.1%
40011
0.1%
38501
0.1%
38451
0.1%
38251
0.1%
38001
0.1%
35001
0.1%

Interactions

2022-03-15T09:47:02.060662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:45:56.576173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:45:59.921284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:03.570390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:06.888527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:10.240527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:14.000535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:17.334502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:20.654150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:24.064922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:27.462548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:31.189256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:34.603279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:38.036661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:41.561309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:44.997109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:48.781440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:52.202942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:56.029758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:47:02.399756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:45:56.750723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:00.109169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:03.750435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:07.069083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:10.409130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:14.174076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:17.507076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:20.834226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:24.241530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:27.636277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:31.377297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:34.790804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:38.219181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:41.736878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:45.175120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:48.968513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:52.387284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:56.418874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:47:02.662053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:45:56.926771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:00.281269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:03.919013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:07.225292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:10.573589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:14.334695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:17.679184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:21.000143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:24.418185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:27.798370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:31.546279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:34.951816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:38.435139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:41.916047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:45.349264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:49.138111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:52.569898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:56.772475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:47:03.000188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:45:57.110281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:00.448527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:04.089049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:07.387013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:10.732011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:14.495078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:17.840794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:21.186931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:24.585737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:27.967167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:31.714550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:35.123811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:38.599713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:42.086543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:45.518178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:49.319423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:52.762173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:57.020014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:47:03.412083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:45:57.272112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:00.880059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:04.253126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:07.548187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:10.893616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:14.668613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:18.008955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:21.391828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:24.758322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:28.138760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:31.881160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:35.288426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:38.766533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:42.258322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:45.696689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:49.503428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:52.946542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:57.215039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:47:03.662415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:45:57.447710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:01.038382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:04.420236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:07.709234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:11.051289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:14.837221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:18.179631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:21.566279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:24.940426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:28.319991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:32.052754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:35.449007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:38.941150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:42.423247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:45.854775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:49.687075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:53.147526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:57.593753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:47:03.889325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:45:57.619746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:01.204912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:04.587146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:07.872314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:11.213897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:15.002093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:18.340399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:21.742712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:25.113970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:28.484144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:32.225564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:35.612578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:39.101735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:42.594754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:46.025293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:49.865462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:53.327386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:57.790949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:47:04.239925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:45:57.788667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:01.382959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:04.754415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:08.045872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:11.375816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:15.166174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:18.559869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:21.918560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-15T09:45:59.729257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:03.392707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:06.705884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:10.053508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:13.760124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:17.138724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:20.473994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:23.874381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:27.272535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:31.001203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:34.421504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:37.852285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:41.340745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:44.795248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:48.601028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:51.996922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:46:55.749947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-15T09:47:01.462201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-15T09:47:21.342965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-15T09:47:21.642164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-15T09:47:21.934424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-15T09:47:22.215670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-15T09:47:22.478962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-15T09:47:06.786758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-15T09:47:08.463501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-15T09:47:09.182938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-15T09:47:09.978442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

SpeciesOwnerCountry.of.OriginFarm.NameLot.NumberMillICO.NumberCompanyAltitudeRegionProducerNumber.of.BagsBag.WeightIn.Country.PartnerHarvest.YearGrading.DateOwner.1VarietyProcessing.MethodAromaFlavorAftertasteAcidityBodyBalanceUniformityClean.CupSweetnessCupper.PointsTotal.Cup.PointsMoistureCategory.One.DefectsQuakersColorCategory.Two.DefectsExpirationCertification.BodyCertification.AddressCertification.Contactunit_of_measurementaltitude_low_metersaltitude_high_metersaltitude_mean_meters
0Arabicametad plcEthiopiametad plcNaNmetad plc2014/2015metad agricultural developmet plc1950-2200guji-hambelaMETAD PLC30060 kgMETAD Agricultural Development plc2014April 4th, 2015metad plcNaNWashed / Wet8.678.838.678.758.508.4210.0010.010.008.7590.580.1200.0Green0April 3rd, 2016METAD Agricultural Development plc309fcf77415a3661ae83e027f7e5f05dad786e4419fef5a731de2db57d16da10287413f5f99bc2ddm1950.02200.02075.0
1Arabicametad plcEthiopiametad plcNaNmetad plc2014/2015metad agricultural developmet plc1950-2200guji-hambelaMETAD PLC30060 kgMETAD Agricultural Development plc2014April 4th, 2015metad plcOtherWashed / Wet8.758.678.508.588.428.4210.0010.010.008.5889.920.1200.0Green1April 3rd, 2016METAD Agricultural Development plc309fcf77415a3661ae83e027f7e5f05dad786e4419fef5a731de2db57d16da10287413f5f99bc2ddm1950.02200.02075.0
2Arabicagrounds for health adminGuatemalasan marcos barrancas "san cristobal cuchNaNNaNNaNNaN1600 - 1800 mNaNNaN51Specialty Coffee AssociationNaNMay 31st, 2010Grounds for Health AdminBourbonNaN8.428.508.428.428.338.4210.0010.010.009.2589.750.0000.0NaN0May 31st, 2011Specialty Coffee Association36d0d00a3724338ba7937c52a378d085f2172daa0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660m1600.01800.01700.0
3Arabicayidnekachew dabessaEthiopiayidnekachew dabessa coffee plantationNaNwolensuNaNyidnekachew debessa coffee plantation1800-2200oromiaYidnekachew Dabessa Coffee Plantation32060 kgMETAD Agricultural Development plc2014March 26th, 2015Yidnekachew DabessaNaNNatural / Dry8.178.588.428.428.508.2510.0010.010.008.6789.000.1100.0Green2March 25th, 2016METAD Agricultural Development plc309fcf77415a3661ae83e027f7e5f05dad786e4419fef5a731de2db57d16da10287413f5f99bc2ddm1800.02200.02000.0
4Arabicametad plcEthiopiametad plcNaNmetad plc2014/2015metad agricultural developmet plc1950-2200guji-hambelaMETAD PLC30060 kgMETAD Agricultural Development plc2014April 4th, 2015metad plcOtherWashed / Wet8.258.508.258.508.428.3310.0010.010.008.5888.830.1200.0Green2April 3rd, 2016METAD Agricultural Development plc309fcf77415a3661ae83e027f7e5f05dad786e4419fef5a731de2db57d16da10287413f5f99bc2ddm1950.02200.02075.0
5Arabicaji-ae ahnBrazilNaNNaNNaNNaNNaNNaNNaNNaN10030 kgSpecialty Coffee Institute of Asia2013September 3rd, 2013Ji-Ae AhnNaNNatural / Dry8.588.428.428.508.258.3310.0010.010.008.3388.830.1100.0Bluish-Green1September 3rd, 2014Specialty Coffee Institute of Asia726e4891cf2c9a4848768bd34b668124d12c4224b70da261fcc84831e3e9620c30a8701540abc200mNaNNaNNaN
6Arabicahugo valdiviaPeruNaNNaNhvcNaNrichmond investment-coffee departmentNaNNaNHVC10069 kgSpecialty Coffee Institute of Asia2012September 17th, 2012Hugo ValdiviaOtherWashed / Wet8.428.508.338.508.258.2510.0010.010.008.5088.750.1100.0Bluish-Green0September 17th, 2013Specialty Coffee Institute of Asia726e4891cf2c9a4848768bd34b668124d12c4224b70da261fcc84831e3e9620c30a8701540abc200mNaNNaNNaN
7Arabicaethiopia commodity exchangeEthiopiaaolmeNaNc.p.w.e010/0338NaN1570-1700oromiaBazen Agricultural & Industrial Dev't Plc30060 kgEthiopia Commodity ExchangeMarch 2010September 2nd, 2010Ethiopia Commodity ExchangeNaNNaN8.258.338.508.428.338.5010.0010.09.339.0088.670.0300.0NaN0September 2nd, 2011Ethiopia Commodity Exchangea176532400aebdc345cf3d870f84ed3ecab6249e61bbaf6a9f341e5782b8e7bd3ebf76aac89fe24bm1570.01700.01635.0
8Arabicaethiopia commodity exchangeEthiopiaaolmeNaNc.p.w.e010/0338NaN1570-1700oromiyaBazen Agricultural & Industrial Dev't Plc30060 kgEthiopia Commodity ExchangeMarch 2010September 2nd, 2010Ethiopia Commodity ExchangeNaNNaN8.678.678.588.428.338.429.3310.09.338.6788.420.0300.0NaN0September 2nd, 2011Ethiopia Commodity Exchangea176532400aebdc345cf3d870f84ed3ecab6249e61bbaf6a9f341e5782b8e7bd3ebf76aac89fe24bm1570.01700.01635.0
9Arabicadiamond enterprise plcEthiopiatulla coffee farmNaNtulla coffee farm2014/15diamond enterprise plc1795-1850snnp/kaffa zone,gimboweredaDiamond Enterprise Plc5060 kgMETAD Agricultural Development plc2014March 30th, 2015Diamond Enterprise PlcOtherNatural / Dry8.088.588.508.507.678.4210.0010.010.008.5088.250.1000.0Green4March 29th, 2016METAD Agricultural Development plc309fcf77415a3661ae83e027f7e5f05dad786e4419fef5a731de2db57d16da10287413f5f99bc2ddm1795.01850.01822.5

Last rows

SpeciesOwnerCountry.of.OriginFarm.NameLot.NumberMillICO.NumberCompanyAltitudeRegionProducerNumber.of.BagsBag.WeightIn.Country.PartnerHarvest.YearGrading.DateOwner.1VarietyProcessing.MethodAromaFlavorAftertasteAcidityBodyBalanceUniformityClean.CupSweetnessCupper.PointsTotal.Cup.PointsMoistureCategory.One.DefectsQuakersColorCategory.Two.DefectsExpirationCertification.BodyCertification.AddressCertification.Contactunit_of_measurementaltitude_low_metersaltitude_high_metersaltitude_mean_meters
1329Robustanitubaasa ltdUgandakigezi coffee farmers associationNaNnitubaasa0nitubaasa ltd1745westernKigezi Coffee Farmers Association2060 kgUganda Coffee Development Authority2013June 27th, 2014Nitubaasa LtdNaNNaN7.837.587.337.677.507.5010.0010.007.757.4280.580.1200.0Green2June 27th, 2015Uganda Coffee Development Authoritye36d0270932c3b657e96b7b0278dfd85dc0fe74303077a1c6bac60e6f514691634a7f6eb5c85aae8m1745.01745.01745.0
1330Robustamannya coffee projectUgandamannya coffee projectNaNmannya coffee project0mannya coffee project1200southernMannya coffee project660 kgUganda Coffee Development Authority2013June 27th, 2014Mannya coffee projectNaNNaN7.757.427.337.587.677.5810.0010.007.677.5080.500.1200.0Green1June 27th, 2015Uganda Coffee Development Authoritye36d0270932c3b657e96b7b0278dfd85dc0fe74303077a1c6bac60e6f514691634a7f6eb5c85aae8m1200.01200.01200.0
1331Robustaandrew hetzelIndiasethuraman estatesNaNNaNNaNcafemakers750mchikmagalurNishant Gurjer1002 kgSpecialty Coffee Association2014May 19th, 2014Andrew HetzelNaNNaN7.677.677.507.337.587.5010.0010.007.427.5080.170.0000.0Bluish-Green1May 19th, 2015Specialty Coffee Associationff7c18ad303d4b603ac3f8cff7e611ffc735e720352d0cf7f3e9be14dad7df644ad65efc27605ae2m750.0750.0750.0
1332Robustaandrew hetzelIndiasethuraman estatesNaNsethuraman estatesNaNcafemakers, llc750mchikmagalurNishant Gurjer2502 kgSpecialty Coffee Association2013June 20th, 2013Andrew HetzelNaNNatural / Dry7.587.427.427.837.427.5010.0010.007.427.5880.170.0000.0Green0June 20th, 2014Specialty Coffee Associationff7c18ad303d4b603ac3f8cff7e611ffc735e720352d0cf7f3e9be14dad7df644ad65efc27605ae2m750.0750.0750.0
1333Robustaandrew hetzelUnited Statessethuraman estatesNaNsethuraman estatesNaNcafemakers, llc3000'chikmagalurSethuraman Estates1001 kgSpecialty Coffee Association2012February 29th, 2012Andrew HetzelArushaNatural / Dry7.927.507.427.427.427.429.3310.007.587.3379.330.0000.0Green0February 28th, 2013Specialty Coffee Associationff7c18ad303d4b603ac3f8cff7e611ffc735e720352d0cf7f3e9be14dad7df644ad65efc27605ae2m3000.03000.03000.0
1334Robustaluis roblesEcuadorrobustasaLavado 1our own labNaNrobustasaNaNsan juan, playasCafé Robusta del Ecuador S.A.12 kgSpecialty Coffee Association2016January 19th, 2016Luis RoblesNaNNaN7.757.587.337.585.087.8310.0010.007.757.8378.750.0000.0Blue-Green1January 18th, 2017Specialty Coffee Associationff7c18ad303d4b603ac3f8cff7e611ffc735e720352d0cf7f3e9be14dad7df644ad65efc27605ae2mNaNNaNNaN
1335Robustaluis roblesEcuadorrobustasaLavado 3own laboratoryNaNrobustasa40san juan, playasCafé Robusta del Ecuador S.A.12 kgSpecialty Coffee Association2016January 19th, 2016Luis RoblesNaNNaN7.507.677.757.755.175.2510.0010.008.428.5878.080.0000.0Blue-Green0January 18th, 2017Specialty Coffee Associationff7c18ad303d4b603ac3f8cff7e611ffc735e720352d0cf7f3e9be14dad7df644ad65efc27605ae2m40.040.040.0
1336Robustajames mooreUnited Statesfazenda cazengoNaNcafe cazengoNaNglobal opportunity fund795 meterskwanza norte province, angolaCafe Cazengo11 kgSpecialty Coffee Association2014December 23rd, 2014James MooreNaNNatural / Dry7.337.337.177.427.507.179.339.337.427.1777.170.0000.0NaN6December 23rd, 2015Specialty Coffee Associationff7c18ad303d4b603ac3f8cff7e611ffc735e720352d0cf7f3e9be14dad7df644ad65efc27605ae2m795.0795.0795.0
1337Robustacafe politicoIndiaNaNNaNNaN14-1118-2014-0087cafe politicoNaNNaNNaN15 lbsSpecialty Coffee Association2013August 25th, 2014Cafe PoliticoNaNNatural / Dry7.426.836.757.177.257.009.339.337.086.9275.080.10200.0Green1August 25th, 2015Specialty Coffee Associationff7c18ad303d4b603ac3f8cff7e611ffc735e720352d0cf7f3e9be14dad7df644ad65efc27605ae2mNaNNaNNaN
1338Robustacafe politicoVietnamNaNNaNNaNNaNcafe politicoNaNNaNNaN15 lbsSpecialty Coffee Association2013August 25th, 2014Cafe PoliticoNaNNatural / Dry6.756.676.506.836.926.839.339.336.677.9273.750.12630.0None9August 25th, 2015Specialty Coffee Associationff7c18ad303d4b603ac3f8cff7e611ffc735e720352d0cf7f3e9be14dad7df644ad65efc27605ae2mNaNNaNNaN